Towards Environment Independent Device Free Human Activity Recognition
Open accessAbstract
Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works
Adversarial Discriminative Domain Adaptation
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.316 · Cited by 5,154 · see citing works
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242 · see citing works
Halperin, D., Hu, W., Sheth, A. N., & Wetherall, D. J. (2011) · ACM SIGCOMM Computer Communication Review · doi:10.1145/1925861.1925870 · Cited by 1,772 · see citing works
Semi-supervised Learning by Entropy Minimization
Grandvalet, Y., & Bengio, Y. (2004) · Cited by 1,585 · see citing works
View invariant human action recognition using histograms of 3D joints
Xia, L., Chen, C.-C., & Aggarwal, J. K. (2012) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2012.6239233 · Cited by 1,512 · see citing works
Simultaneous Deep Transfer Across Domains and Tasks
Tzeng, E., Hoffman, J., Darrell, T., & Saenko, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.463 · Cited by 1,203 · see citing works
Domain-adversarial training of neural networks
GaninYaroslav, UstinovaEvgeniya, AjakanHana, GermainPascal, LarochelleHugo, LavioletteFrançois, MarchandMario, & LempitskyVictor (2016) · Journal of Machine Learning Research · doi:10.5555/2946645.2946704 · Cited by 1,203 · see citing works
Understanding and Modeling of WiFi Signal Based Human Activity Recognition
Wang, W., Liu, A. X., Shahzad, M., Ling, K., & Lu, S. (2015) · Proceedings of the 21st Annual International Conference on Mobile Computing and Networking · doi:10.1145/2789168.2790093 · Cited by 1,110 · see citing works
Soli: ubiquitous gesture sensing with millimeter wave radar
Lien, J., Gillian, N., Karagozler, M. E., Amihood, P., Schwesig, C., Olson, E., Raja, H., & Poupyrev, I. (2016) · ACM Transactions on Graphics · doi:10.1145/2897824.2925953 · Cited by 957 · see citing works
Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014) · Proceedings of the 6th International Conference on Mobile Computing, Applications and Services · doi:10.4108/icst.mobicase.2014.257786 · Cited by 876 · see citing works
Wang, Y., Liu, J., Chen, Y. J., Gruteser, M., Yang, J., & Liu, H. (2014) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2639108.2639143 · Cited by 817 · see citing works
Self-citationNeural Networks: Tricks of the Trade
Orr, G., & Müller, K.-R. (1998) · Lecture notes in computer science · doi:10.1007/3-540-49430-8 · Cited by 817 · see citing works
The identification of multiple outliers
Davies, L., & Gather, U. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476339 · Cited by 709 · see citing works
Keystroke Recognition Using WiFi Signals
Ali, K., Liu, A. X., Wang, W., & Shahzad, M. (2015) · ACM/IEEE International Conference on Mobile Computing and Networking · doi:10.1145/2789168.2790109 · Cited by 582 · see citing works
Deep Learning via Semi-supervised Embedding
Weston, J., Ratle, F., Mobahi, H., & Collobert, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_34 · Cited by 577 · see citing works
WiGest: A ubiquitous WiFi-based gesture recognition system
Abdelnasser, H., Youssef, M. A., & Harras, K. A. (2015) · doi:10.1109/infocom.2015.7218525 · Cited by 556 · see citing works
A DIRT-T Approach to Unsupervised Domain AdaptationPreprint
Shu, R., Bui, H., Narui, H., & Ermon, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08735 · Cited by 506 · see citing works
Adversarial Discriminative Domain AdaptationPreprint
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05464 · Cited by 485 · see citing works
Device-free gesture tracking using acoustic signals
Wang, W., Liu, A. X., & Sun, K. (2016) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/2973750.2973764 · Cited by 382 · see citing works
Gupta, S., Morris, D., Patel, S., et al. (2012) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/2207676.2208331 · Cited by 356 · see citing works
Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT
Shi, C., Liu, J., Liu, H., & Chen, Y. J. (2017) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/3084041.3084061 · Cited by 295 · see citing works
Li, H., Yang, W., Wang, J., Xu, Y., & Huang, L. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971738 · Cited by 271 · see citing works
Wei, T., & Zhang, X. (2015) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/2789168.2790113 · Cited by 266 · see citing works
Domain-Adversarial Neural NetworksPreprint
Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., & Marchand, M. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.4446 · Cited by 262 · see citing works
Position and Orientation Agnostic Gesture Recognition Using WiFi
Virmani, A., & Shahzad, M. (2017) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3081333.3081340 · Cited by 257 · see citing works
Tan, S., & Yang, J. (2016) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2942358.2942393 · Cited by 256 · see citing works
Device-Free Wireless Localization and Activity Recognition: A Deep Learning Approach
Wang, J., Zhang, X., Gao, Q., Yue, H., & Wang, H. (2016) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2016.2635161 · Cited by 255 · see citing works
Wang, G., Zou, Y., Zhou, Z., Wu, K., & Ni, L. M.-S. (2016) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2016.2517630 · Cited by 236 · see citing works
Wu, D., Zhang, D., Xu, C., Wang, Y., & Wang, H. E. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971658 · Cited by 206 · see citing works
Self-citationMulti-User Gesture Recognition Using WiFi
Venkatnarayan, R. H., Page, G., & Shahzad, M. (2018) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3210240.3210335 · Cited by 195 · see citing works
Monitoring vital signs using millimeter wave
Yang, Z., Pathak, P. H., Zeng, Y., Liran, X., & Mohapatra, P. (2016) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2942358.2942381 · Cited by 189 · see citing works
WiG: WiFi-Based Gesture Recognition System
He, W., Wu, K., Zou, Y., & Ming, Z. (2015) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2015.7288485 · Cited by 186 · see citing works
Reflection and transmission behaviour of building materials at 60 GHz
Langen, B., Lober, G., & Herzig, W. (2002) · doi:10.1109/wncmf.1994.529141 · Cited by 157 · see citing works
Extended high-frequency (9–20 kHz) audiometry reference thresholds in 645 healthy subjects
Valiente, A. R., Trinidad, A., García‐Berrocal, J. R., et al. (2014) · International Journal of Audiology · doi:10.3109/14992027.2014.893375 · Cited by 156 · see citing works
Keally, M., Zhou, G., Xing, G., Wu, J., & Pyles, A. J. (2011) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2070942.2070968 · Cited by 155 · see citing works
Ruan, W., Sheng, Q. Z., Yang, L., et al. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971736 · Cited by 153 · see citing works
Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs
Khan, A., Tufail, A., Khattak, A. M., & Laine, T. H. (2014) · International Journal of Distributed Sensor Networks · doi:10.1155/2014/503291 · Cited by 149 · see citing works
Practical Human Sensing in the Light
Li, T., Liu, Q., & Zhou, X. (2016) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2906388.2906401 · Cited by 135 · see citing works
Reusing 60GHz Radios for Mobile Radar Imaging
Zhu, Y., Zhu, Y., Zhao, B. Y., & Zheng, H. (2015) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/2789168.2790112 · Cited by 130 · see citing works
Leveraging RF-channel fluctuation for activity recognition
Sigg, S., Shi, S., Büsching, F., Ji, Y., & Wolf, L. C. (2013) · International Conference on Advances in Mobile Computing and Multimedia (MoMM) · doi:10.1145/2536853.2536873 · Cited by 109 · see citing works
Chen, K.-Y., Ashbrook, D., Goel, M., et al. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632090 · Cited by 96 · see citing works
Boon and bane of 60 GHz networks
Nitsche, T., Bielsa, G., Tejado, I., et al. (2015) · doi:10.1145/2716281.2836102 · Cited by 96 · see citing works
WiDriver: Driver Activity Recognition System Based on WiFi CSI
Duan, S., Tian-qing, Y., & He, J. (2018) · International Journal of Wireless Information Networks · doi:10.1007/s10776-018-0389-0 · Cited by 75 · see citing works
WiFi-Enabled Smart Human Dynamics Monitoring
Guo, X., Liu, B., Shi, C., Liu, H., Chen, Y. J., & Chuah, M. C. (2017) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3131672.3131692 · Cited by 70 · see citing works
Dolphin: Ultrasonic-Based Gesture Recognition on Smartphone Platform
Qifan, Y., Tang, H., Zhao, X., et al. (2014) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2014.273 · Cited by 68 · see citing works
Zhang, O., & Srinivasan, K. (2016) · doi:10.1145/2999572.2999582 · Cited by 63 · see citing works
Device-Free Wireless Sensing in Complex Scenarios Using Spatial Structural Information
Wang, J., Zhang, L., Gao, Q., Pan, M., & Wang, H. (2018) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2018.2796086 · Cited by 62 · see citing works
Reconstructing Hand Poses Using Visible Light
Li, T., Xiong, X., Xie, Y., Hito, G., Yang, X.-D., & Zhou, X. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130937 · Cited by 57 · see citing works
Fast and Infuriating: Performance and Pitfalls of 60 GHz WLANs Based on Consumer-Grade Hardware
Saha, S. K., Assasa, H., Loch, A., et al. (2018) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/sahcn.2018.8397123 · Cited by 54 · see citing works
Recognizing human motions through mixture modeling of inertial data
Field, M., Stirling, D., Pan, Z. S., et al. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.004 · Cited by 48 · see citing works
Saha, S. K., Ghasempour, Y., Haider, M. K., et al. (2017) · doi:10.1145/3131473.3131479 · Cited by 47 · see citing works
Device-free and device-bound activity recognition using radio signal strength
Scholz, M., Riedel, T., Hock, M., & Beigl, M. (2013) · Augmented Human International Conference · doi:10.1145/2459236.2459254 · Cited by 43 · see citing works
Zhu, Y., Zhu, Y., Zhang, Z., Zhao, B. Y., & Zheng, H. (2015) · IEEE Workshop on Mobile Computing Systems and Applications (WMCSA) · doi:10.1145/2699343.2699363 · Cited by 29 · see citing works
A Robust Sign Language Recognition System with Sparsely Labeled Instances Using Wi-Fi Signals
Shang, J., & Wu, J. (2017) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass.2017.41 · Cited by 28 · see citing works
Visible Light Knows Who You Are
An, C., Li, T., Zhao, T. Q., et al. (2015) · doi:10.1145/2801073.2801078 · Cited by 25 · see citing works
SleepSense: Non-invasive sleep event recognition using an electromagnetic probe
Zhuang, Y., Song, C. L., Wang, A., Lin, F., Li, Y., Gu, C., Li, C., & Xu, W. (2015) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2015.7299364 · Cited by 10 · see citing works
Self-citationTowards Personalized Learning in Mobile Sensing Systems
Jiang, W., Li, Q., Su, L., Miao, C., Gu, Q., & Xu, W. (2018) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2018.00040 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 15
- [5]5. Robert Bodor, Bennett Jackson, and Nikolaos Papanikolopoulos. 2003. Vision-based human tracking and activity recognition. In Proc. of the 11th Mediterranean Conf. on Control and Automation, Vol. 1.
- [6]6. Ke-Yu Chen, Daniel Ashbrook, Mayank Goel, Sung-Hyuck Lee, and Shwetak Patel. 2014. AirLink: sharing files between multiple devices using in-air gestures. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 565-569.
- [10]10. Yaroslav Ganin and Victor Lempitsky. 2015. Unsupervised domain adaptation by backpropagation. In International Conference on Machine Learning. 1180-1189.
- [12]12. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information process- ing systems. 2672-2680.
- [15]15. Sidhant Gupta, Daniel Morris, Shwetak Patel, and Desney Tan. 2012. Soundwave: using the doppler effect to sense gestures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1911-1914.
- [20]20. Matthew Keally, Gang Zhou, Guoliang Xing, Jianxin Wu, and An- drew Pyles. 2011. Pbn: towards practical activity recognition using smartphone-based body sensor networks. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. ACM, 246- 259.
- [24]24. Hong Li, Wei Yang, Jianxin Wang, Yang Xu, and Liusheng Huang. 2016. WiFinger: talk to your smart devices with finger-grained gesture. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 250-261.
- [33]33. Wenjie Ruan, Quan Z Sheng, Lei Yang, Tao Gu, Peipei Xu, and Longfei Shangguan. 2016. AudioGest: enabling fine-grained hand gesture detection by decoding echo signal. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 474-485.
- [35]35. Swetank Kumar Saha, Yasaman Ghasempour, Muhammad Kumail Haider, Tariq Siddiqui, Paulo De Melo, Neerad Somanchi, Luke Zakra- jsek, Arjun Singh, Owen Torres, Daniel Uvaydov, Josep Miquel Jornet, Edward Knightly, Dimitrios Koutsonikolas, Dimitris Pados, and Zhi Sun. 2017. X60: A programmable testbed for wideband 60 ghz wlans with phased arrays. In Proceedings of the 11th Workshop on Wireless Network Testbeds, Experimental evaluation & CHaracterization. ACM, 75-82.
- [42]42. Sheng Tan and Jie Yang. 2016. WiFinger: leveraging commodity WiFi for fine-grained finger gesture recognition. In Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Com- puting. ACM, 201-210.
- [52]52. Yan Wang, Jian Liu, Yingying Chen, Marco Gruteser, Jie Yang, and Hongbo Liu. 2014. E-eyes: device-free location-oriented activity iden- tification using fine-grained wifi signatures. In Proceedings of the 20th annual international conference on Mobile computing and networking. ACM, 617-628.
- [53]53. Teng Wei and Xinyu Zhang. 2015. mtrack: High-precision passive tracking using millimeter wave radios. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, 117-129.
- [55]55. Dan Wu, Daqing Zhang, Chenren Xu, Yasha Wang, and Hao Wang. 2016. WiDir: walking direction estimation using wireless signals. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. ACM, 351-362.
- [59]59. Ouyang Zhang and Kannan Srinivasan. 2016. Mudra: User-friendly Fine-grained Gesture Recognition using WiFi Signals. In Proceedings of the 12th International on Conference on emerging Networking EXperi- ments and Technologies. ACM, 83-96.
- [60]60. Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S Jaakkola, and Matt T Bianchi. 2017. Learning sleep stages from radio signals: A conditional adversarial architecture. In International Conference on Machine Learning. 4100-4109.
Cited by
Sorted by citations
Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi
Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., & Yang, Z. (2019) · Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3307334.3326081 · Cited by 557 · see citing works
Deep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690 · see citing works
“uilding classification models for device-free activity recognition, very few works focus on how to get equally accurate recognition performance when sensors are configured in the wild. One example is [66], where an adversarial network is incorporated with deep feature extraction models to remove the environment-specif…”
BackgroundDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · see citing works
“uilding classification models for device-free activity recognition, very few works focus on how to get equally accurate recognition performance when sensors are configured in the wild. One example is [66], where an adversarial network is incorporated with deep feature extraction models to remove the environment-specif…”
BackgroundA Survey on Human Behavior Recognition Using Channel State Information
Wang, Z., Jiang, K., Hou, Y., Dou, W., Zhang, C., Huang, Z., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2949123 · Cited by 149 · see citing works
Xu, H., Zhou, P., Tan, R., Li, M., & Shen, G. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/3485730.3485937 · Cited by 150 · see citing works
Cosmo: contrastive fusion learning with small data for multimodal human activity recognition
Ouyang, X., Shuai, X., Zhou, J., Shi, I. W., Xie, Z., Xing, G., & Huang, J. (2022) · Proceedings of the 28th Annual International Conference on Mobile Computing And Networking · doi:10.1145/3495243.3560519 · Cited by 94 · see citing works
A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM
Gao, X., Luo, H., Wang, Q., Zhao, F., Ye, L., & Zhang, Y. (2019) · Sensors · doi:10.3390/s19040947 · Cited by 92 · see citing works
XGest: Enabling Cross-Label Gesture Recognition with RF Signals
Zhang, Y., Yang, Z., Zhang, G., Wu, C., & Zhang, L. (2021) · ACM Transactions on Sensor Networks · doi:10.1145/3458750 · Cited by 29 · see citing works
Towards Customizable Foundation Models for Human Activity Recognition with Wearable Devices
Qiu, M., Weng, C., Fan, M., & Wu, K. (2025) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3749479 · Cited by 4 · see citing works
“Additionally, HAR systems incorporate vision-based [32], wireless-based [16, 21, 50, 61, 66, 67], and mobile-based [17, 24, 27, 38, 40, 41, 43, 55, 57, 59] methods.”
- Authors
- Jiang, W., Miao, C., Ma, F., Yao, S., Wang, Y., Yuan, Y., Xue, H., Song, C., Ma, X., Koutsonikolas, D., Xu, W., & Su, L.
- Affiliations
- University at Buffalo, State University of New York (US)
- University of Illinois Urbana-Champaign (US)
- Beijing University of Technology (CN)
- State University of New York at Buffalo, Buffalo, NY, USA
- ORCID
- Venue
- Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
- Publisher
- ACM
- Published
- 2018-10-15
- Pages
- 289-304
- ISSN
- 1543-5679 (ISSN-L)
- OpenAlex
- W2897132279
- Cited by
- 573
- References
- 62
- Citations by year
- 2026: 42 · 2025: 75 · 2024: 93 · 2023: 79 · 2022: 85 · 2021: 94 · 2020: 71 · 2019: 32 · 2018: 1 · 2012: 1
- FWCI
- 14.92
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Annual International Conference on Mobile Computing and Networking (MobiCom) (publishedVersion)
- Funders
- National Science Foundation (CNS-1652503,CNS-1629929)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- cross-subject generalization, cross-environment generalization, WiFi sensing, ultrasonic sensing, visible light detection, feature extraction, deep learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
